10 Recent Big Data Technology Developments

The trend in Big Data is changing rapidly all over the world. Over the years, lot has changed and the developments are there for everyone to see and benefit from.

In its absolute bare, Big Data is a term that has been used to describe sets of data, which are so big that they cannot be processed and understood using the traditional methods of gathering them. The usual challenges encountered include analysis, data curation, searching and sharing, storage, transfer, collating and quantifying it. In fact, the use of big data technology is primarily a tool for predictive analysis, to make better decision, to reduce costs and risks.

Here, we take a look at some of the biggest Big Data Technology for the upcoming future:

Latest trends in predictive analysis

In a span of three years, Ford turned its fortunes globally using Big Data analysis. All they did was, to make every major decision based on the insight that they gathered going through the data. Not only did this help them cull their losses but also brought about healthy profits globally within the first three years of introducing this change.

By analysing the available data and the current trends, researchers can now predict the nature of the stock market using big data.

Altiscale has recently launchedthe Altiscale Insight Cloud. It is a self-analytics service that allows users and business analysts to quantify data.

Likewise, AtScale has launched the AtScale Intelligence Platform 4.0 that enables users to use business tools like Tableau, Qlik amongst others to access data stored in Hadoop clusters.

Blue Data has released EPIC that helps to reduce the complexity of implementing technologies like Spark and Hadoop. There is the Domo Business Cloud which is a collection of business management applications that helps decision makers with data insights and access to help find answers to the business issues.

Kyvos Insights’ flagship software now comes with the Azure HDInsight that allows users to deploy tools like Kyvos in analysis.

MapD Technologies has launched a database and visual analytics software that uses GPU-based processing units to map data and assists analysts in making predictions.

Ryft Systems has introduced a new hybrid FPGA cluster called the Ryft One Cluster that claims to accelerate big data ecosystems by a factor of 100 and helps reduce costs by 70%.

Trifacta, another developer of ‘data wrangling’ software, has released the Photon Compute Framework that helps transform raw, complex data to a clean, structured format. This is something that helps data analysers predict future for bigger business with minimum risk.

To come up with something trendy and popular among users is the biggest challenge the app industry is facing. This can be met with big data predictions. Priori Data, a Berlin based company maps these trends in customer preferences as regards to apps. Their platform has the ability to track and scale over 2 million apps in use.

Aspects to be phased out

Even though big data analysis is an important aspect, certain things need to be avoided. For instance, using RDBMS schema as files and creating data ponds can lead to confusion in mining the data as it provides variable answers to the same question. This could eventually create problems for the users. Treating Hbase like an RDBMS or HDFS as a file system, are other issues that can be easily avoided.

Way forward

Data Agility will gain more importance in the near future. Instead of focusing on managing the data, companies will now measure the value of the data that is coming in. More importantly, it will be about analysing how effective this data will be in managing results and predicting the profits a business can make. Data Lakes are out and Data Platforms are in. Data Lakes have actually evolved as platforms for the large scale data processing required by firms. In fact, this has led big data going main stream.